Publications
Selected publications
- The Complexity of the Simplex Method (Conference Paper - 2014)
- Learning equilibria of games via payoff queries (Journal article - 2015)
- The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions (Journal article - 2013)
- Enumeration of Nash equilibria for two-player games (Journal article - 2009)
- Unique end of potential line (Journal article - 2020)
2024
Selfishly Prepaying in Financial Credit Networks
Zhou, H., Wang, Y., Varsos, K., Bishop, N., Savani, R., Calinescu, A., & Wooldridge, M. (2024). Selfishly Prepaying in Financial Credit Networks. The journal of artificial intelligence research, 81.
Market Making with Learned Beta Policies
Wang, Y., Savani, R., Gu, A., Mascioli, C., Turocy, T., & Wellman, M. (2024). Market Making with Learned Beta Policies. In Proceedings of the 5th ACM International Conference on AI in Finance (pp. 643-651). ACM. doi:10.1145/3677052.3698623
The Effect of Liquidity on the Spoofability of Financial Markets
Gu, A., Wang, Y., Mascioli, C., Chakraborty, M., Savani, R., Turocy, T. L., & Wellman, M. P. (2024). The Effect of Liquidity on the Spoofability of Financial Markets. In Proceedings of the 5th ACM International Conference on AI in Finance (pp. 239-247). ACM. doi:10.1145/3677052.3698634
First Order Methods for Geometric Optimization of Crystals: Experimental Analysis
Tsili, A., Dyer, M. S., Gusev, V. V., Krysta, P., & Savani, R. (n.d.). First Order Methods for Geometric Optimization of Crystals: Experimental Analysis. Advanced Theory and Simulations. doi:10.1002/adts.202400124
First Order Methods for Geometric Optimization of Crystals: Theoretical Derivations
Tsili, A., Dyer, M. S., Gusev, V. V., Krysta, P., & Savani, R. (n.d.). First Order Methods for Geometric Optimization of Crystals: Theoretical Derivations. Advanced Theory and Simulations. doi:10.1002/adts.202400125
Two Choices Are Enough for P-LCPs, USOs, and Colorful Tangents
Borzechowski, M., Fearnley, J., Gordon, S., Savani, R., Schnider, P., & Weber, S. (2024). Two Choices Are Enough for P-LCPs, USOs, and Colorful Tangents. In Leibniz International Proceedings in Informatics, LIPIcs Vol. 297. doi:10.4230/LIPIcs.ICALP.2024.32
The Complexity of Computing KKT Solutions of Quadratic Programs
Fearnley, J., Goldberg, P. W., Hollender, A., & Savani, R. (2024). The Complexity of Computing KKT Solutions of Quadratic Programs. Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 892-903. doi:10.1145/3618260.3649647
A Strategic Analysis of Prepayments in Financial Credit Networks
Zhou, H., Wang, Y., Varsos, K., Bishop, N., Savani, R., Calinescu, A., & Wooldridge, M. (2024). A Strategic Analysis of Prepayments in Financial Credit Networks. In Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence (pp. 3040-3048). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2024/337
Ordinal Potential-based Player Rating
Vadori, N., & Savani, R. (2024). Ordinal Potential-based Player Rating. In Proceedings of Machine Learning Research Vol. 238 (pp. 118-126).
Policy Space Response Oracles: A Survey
Bighashdel, A., Wang, Y., McAleer, S., Savani, R., & Oliehoek, F. A. (2024). Policy Space Response Oracles: A Survey. In Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence (pp. 7951-7961). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2024/880
The Complexity of Computing KKT Solutions of Quadratic Programs.
Fearnley, J., Goldberg, P. W., Hollender, A., & Savani, R. (2024). The Complexity of Computing KKT Solutions of Quadratic Programs.. In B. Mohar, I. Shinkar, & R. O'Donnell (Eds.), STOC (pp. 892-903). ACM. Retrieved from https://doi.org/10.1145/3618260
Two Choices Are Enough for P-LCPs, USOs, and Colorful Tangents.
Borzechowski, M., Fearnley, J., Gordon, S., Savani, R., Schnider, P., & Weber, S. (2024). Two Choices Are Enough for P-LCPs, USOs, and Colorful Tangents.. In K. Bringmann, M. Grohe, G. Puppis, & O. Svensson (Eds.), ICALP Vol. 297 (pp. 32:1). Schloss Dagstuhl - Leibniz-Zentrum für Informatik. Retrieved from https://www.dagstuhl.de/dagpub/978-3-95977-322-5
2023
Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness
Coletta, A., Jerome, J., Savani, R., & Vyetrenko, S. (2023). Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness. In 4th ACM International Conference on AI in Finance (pp. 27-35). ACM. doi:10.1145/3604237.3626854
Mbt-gym: Reinforcement learning for model-based limit order book trading
Jerome, J., Sánchez-Betancourt, L., Savani, R., & Herdegen, M. (2023). Mbt-gym: Reinforcement learning for model-based limit order book trading. In 4th ACM International Conference on AI in Finance (pp. 619-627). ACM. doi:10.1145/3604237.3626873
Recommender Systems and Supplier Competition on Platforms
Fletcher, A., Ormosi, P. L., & Savani, R. (n.d.). Recommender Systems and Supplier Competition on Platforms. Journal of Competition Law and Economics. doi:10.1093/joclec/nhad009
Reinforcement Learning in Crystal Structure Prediction
Zamaraeva, E., Collins, C. M., Antypov, D., Gusev, V. V., Savani, R., Dyer, M. S., . . . Spirakis, P. G. (n.d.). Reinforcement Learning in Crystal Structure Prediction. Digital Discovery. doi:10.1039/d3dd00063j
Reinforcement Learning in Crystal Structure Prediction
Biased Recommender Systems And Supplier Competition
Recommender Systems and Competition on Subscription-Based Platforms
2022
Market Making with Scaled Beta Policies
Jerome, J., Palmer, G., & Savani, R. (2022). Market Making with Scaled Beta Policies. In Proceedings of the Third ACM International Conference on AI in Finance (pp. 214-222). ACM. doi:10.1145/3533271.3561745
A Faster Algorithm for Finding Tarski Fixed Points
Fearnley, J., Palvolgyi, D., & Savani, R. (2022). A Faster Algorithm for Finding Tarski Fixed Points. ACM TRANSACTIONS ON ALGORITHMS, 18(3). doi:10.1145/3524044
A Faster Algorithm for Finding Tarski Fixed Points
Fearnley, J., Pálvölgyi, D., & Savani, R. (2022). A Faster Algorithm for Finding Tarski Fixed Points. ACM Transactions on Algorithms, 18(3), 1-23. doi:10.1145/3524044
Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures
Vadori, N., Savani, R., Spooner, T., & Ganesh, S. (2022). Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures. In INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162. Retrieved from https://www.webofscience.com/
Difference rewards policy gradients
Castellini, J., Devlin, S., Oliehoek, F. A., & Savani, R. (2022). Difference rewards policy gradients. In NEURAL COMPUTING & APPLICATIONS. doi:10.1007/s00521-022-07960-5
Generative Models over Neural Controllers for Transfer Learning
Butterworth, J., Savani, R., & Tuyls, K. (2022). Generative Models over Neural Controllers for Transfer Learning. In Unknown Book (Vol. 13398, pp. 400-413). doi:10.1007/978-3-031-14714-2_28
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent
Gemp, I., Savani, R., Lanctot, M., Bachrach, Y., Anthony, T., Everett, R., . . . Kramár, J. (2022). Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS Vol. 1 (pp. 507-515).
2021
The Complexity of Gradient Descent
Savani, R. (2021). The Complexity of Gradient Descent. In Leibniz International Proceedings in Informatics, LIPIcs Vol. 213. doi:10.4230/LIPIcs.FSTTCS.2021.5
Trading via Selective Classification
Chalkidis, N., & Savani, R. (2021). Trading via Selective Classification. In ICAIF 2021: THE SECOND ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE. doi:10.1145/3490354.3494379
Trading via Selective Classification
Chalkidis, N., & Savani, R. (2021). Trading via Selective Classification. Retrieved from http://dx.doi.org/10.1145/3490354.3494379
Trading via Selective Classification
Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning
Castellini, J., Oliehoek, F. A., Savani, R., & Whiteson, S. (2021). Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning. AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, 35(2). doi:10.1007/s10458-021-09506-w
A deep learning approach to identify unhealthy advertisements in street view images
Palmer, G., Green, M., Boyland, E., Vasconcelos, Y. S. R., Savani, R., & Singleton, A. (2021). A deep learning approach to identify unhealthy advertisements in street view images. SCIENTIFIC REPORTS, 11(1). doi:10.1038/s41598-021-84572-4
Difference Rewards Policy Gradients
Castellini, J., Devlin, S., Oliehoek, F. A., & Savani, R. (2021). Difference Rewards Policy Gradients. In ALA 2021 - Adaptive and Learning Agents Workshop at AAMAS 2021.
Reachability Switching Games.
Fearnley, J., Gairing, M., Mnich, M., & Savani, R. (2021). REACHABILITY SWITCHING GAMES. In LOGICAL METHODS IN COMPUTER SCIENCE Vol. 17. doi:10.23638/LMCS-17(2:10)2021
2020
Difference Rewards Policy Gradients
Difference Rewards Policy Gradients
Castellini, J., Devlin, S., Oliehoek, F. A., & Savani, R. (2020). Difference Rewards Policy Gradients. Retrieved from http://dx.doi.org/10.1007/s00521-022-07960-5
Unique end of potential line
Fearnley, J. S., Gordon, S., Mehta, R., & Savani, R. (2020). Unique End of Potential Line. Journal of Computer and System Sciences, 114, 1-35. doi:10.1016/j.jcss.2020.05.007
The Complexity of Gradient Descent: CLS = PPAD $\cap$ PLS
The complexity of gradient descent: CLS = PPAD ∩ PLS
Fearnley, J., Goldberg, P. W., Hollender, A., & Savani, R. (2021). The Complexity of Gradient Descent: CLS = PPAD ∧ PLS. STOC '21: PROCEEDINGS OF THE 53RD ANNUAL ACM SIGACT SYMPOSIUM ON THEORY OF COMPUTING, 46-59. doi:10.1145/3406325.3451052
A Faster Algorithm for Finding Tarski Fixed Points
Fearnley, J., & Savani, R. (2021). A Faster Algorithm for Finding Tarski Fixed Points. 38TH INTERNATIONAL SYMPOSIUM ON THEORETICAL ASPECTS OF COMPUTER SCIENCE (STACS 2021), 187. doi:10.4230/LIPIcs.STACS.2021.29
A faster algorithm for finding Tarski fixed points
Bayesian optimisation of restriction zones for bluetongue control.
Spooner, T., Jones, A. E., Fearnley, J., Savani, R., Turner, J., & Baylis, M. (2020). Bayesian optimisation of restriction zones for bluetongue control.. Scientific Reports, 10(1), 15139. doi:10.1038/s41598-020-71856-4
A deep learning approach to identify unhealthy advertisements in street view images
One-Clock Priced Timed Games are PSPACE-hard
Fearnley, J., Ibsen-Jensen, R., & Savani, R. (2020). One-Clock Priced Timed Games are PSPACE-hard. LICS '20: Proceedings of the 35th Annual ACM/IEEE Symposium on Logic in Computer Science. Retrieved from http://arxiv.org/abs/2001.04458v2
Mapping the geodemographics of digital inequality in Great Britain: An integration of machine learning into small area estimation
Singleton, A., Alexiou, A., & Savani, R. (2020). Mapping the geodemographics of digital inequality in Great Britain: An integration of machine learning into small area estimation. Computers, Environment and Urban Systems, 82, 101486. doi:10.1016/j.compenvurbsys.2020.101486
Tree Polymatrix Games are PPAD-hard
Tree Polymatrix Games are PPAD-hard
Deligkas, A., Fearnley, J., & Savani, R. (2020). Tree Polymatrix Games are PPAD-hard. Retrieved from http://arxiv.org/abs/2002.12119v1
The Automated Inspection of Opaque Liquid Vaccines
Palmer, G., Schnieders, B., Savani, R., Tuyls, K., Fossel, J., & Flore, H. (2020). The Automated Inspection of Opaque Liquid Vaccines. In ECAI 2020: 24TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE Vol. 325 (pp. 1898-1905). doi:10.3233/FAIA200307
The Automated Inspection of Opaque Liquid Vaccines
One-Clock Priced Timed Games are PSPACE-hard
Robust market making via adversarial reinforcement learning
Spooner, T., & Savani, R. (2020). Robust Market Making via Adversarial Reinforcement Learning. In PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 4590-4596). Retrieved from https://www.webofscience.com/
Tree Polymatrix Games Are PPAD-Hard.
Deligkas, A., Fearnley, J., & Savani, R. (2020). Tree Polymatrix Games Are PPAD-Hard.. In A. Czumaj, A. Dawar, & E. Merelli (Eds.), ICALP Vol. 168 (pp. 38:1). Schloss Dagstuhl - Leibniz-Zentrum für Informatik. Retrieved from https://www.dagstuhl.de/dagpub/978-3-95977-138-2
2019
Evolving indoor navigational strategies using gated recurrent units in NEAT
Butterworth, J., Savani, R., & Tuyls, K. (2019). Evolving indoor navigational strategies using gated recurrent units in NEAT. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 111-112). doi:10.1145/3319619.3321995
Evolving Indoor Navigational Strategies Using Gated Recurrent Units In NEAT
Distributed Methods for Computing Approximate Equilibria
Czumaj, A., Deligkas, A., Fasoulakis, M., Fearnley, J. S., Jurdzinski, M., & Savani, R. (2019). Distributed Methods for Computing Approximate Equilibria. Algorithmica: an international journal in computer science, 81, 1205-1231. doi:10.1007/s00453-018-0465-y
Analysing Factorizations of Action-Value Networks for Cooperative Multi-Agent Reinforcement Learning
The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning
Castellini, J., Oliehoek, F. A., Savani, R., & Whiteson, S. (2019). The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning. In AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (pp. 1862-1864). Retrieved from https://www.webofscience.com/
Computing Stable Outcomes in Symmetric Additively Separable Hedonic Games.
Gairing, M., & Savani, R. (2019). Computing Stable Outcomes in Symmetric Additively Separable Hedonic Games.. Math. Oper. Res., 44, 1101-1121. doi:10.1287/moor.2018.0960
2018
Unique End of Potential Line
Fearnley, J., Gordon, S., Mehta, R., & Savani, R. (2018). Unique End of Potential Line. Retrieved from http://arxiv.org/abs/1811.03841v1
Unique End of Potential Line
The Complexity of All-Switches Strategy Improvement
Fearnley, J. S., & Savani, R. S. J. (2018). The Complexity of All-Switches Strategy Improvement. Logical Methods in Computer Science, 14(4), 1-57. doi:10.23638/LMCS-14(4:9)2018
Inapproximability results for constrained approximate Nash equilibria
Deligkas, A., Fearnley, J. S., & Savani, R. (2018). Inapproximability results for constrained approximate Nash equilibria. Information and Computation, 262(1), 40-56. doi:10.1016/j.ic.2018.06.001
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
Palmer, G., Savani, R., & Tuyls, K. (2019). Negative Update Intervals in Deep Multi-Agent Reinforcement Learning. In AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (pp. 43-51). Retrieved from https://www.webofscience.com/
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
Beyond Local Nash Equilibria for Adversarial Networks
Oliehoek, F. A., Savani, R., Gallego, J., van der Pol, E., & Gross, R. (2019). Beyond Local Nash Equilibria for Adversarial Networks. In ARTIFICIAL INTELLIGENCE, BNAIC 2018 (Vol. 1021, pp. 73-89). doi:10.1007/978-3-030-31978-6_7
Beyond Local Nash Equilibria for Adversarial Networks
Space Debris Removal: Learning to Cooperate and the Price of Anarchy
Klima, R., Bloembergen, D., Savani, R., Tuyls, K., Wittig, A., Sapera, A., & Izzo, D. (2018). Space Debris Removal: Learning to Cooperate and the Price of Anarchy. Frontiers in Robotics and AI, 5. doi:10.3389/frobt.2018.00054
Market Making via Reinforcement Learning
Spooner, T., Fearnley, J., Savani, R., & Koukorinis, A. (2018). Market Making via Reinforcement Learning. In PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18) (pp. 434-442). Retrieved from https://www.webofscience.com/
Market Making via Reinforcement Learning
End of Potential Line
Fearnley, J., Gordon, S., Mehta, R., & Savani, R. (2018). End of Potential Line. Retrieved from http://arxiv.org/abs/1804.03450v2
End of Potential Line
Symmetric Decomposition of Asymmetric Games
Tuyls, K., Perolat, J., Lanctot, M., Ostrovski, G., Savani, R., Leibo, J., . . . Legg, S. (2018). Symmetric Decomposition of Asymmetric Games. Scientific Reports, 8. doi:10.1038/s41598-018-19194-4
Beyond Local Nash Equilibria for Adversarial Networks.
Oliehoek, F. A., Savani, R., Gallego-Posada, J., Pol, E. V. D., & Groß, R. (2018). Beyond Local Nash Equilibria for Adversarial Networks.. In M. Atzmueller, & W. Duivesteijn (Eds.), BNCAI Vol. 1021 (pp. 73-89). Springer. Retrieved from https://doi.org/10.1007/978-3-030-31978-6
Lenient Multi-Agent Deep Reinforcement Learning.
Palmer, G., Tuyls, K., Bloembergen, D., & Savani, R. (2018). Lenient Multi-Agent Deep Reinforcement Learning.. In E. André, S. Koenig, M. Dastani, & G. Sukthankar (Eds.), AAMAS (pp. 443-451). International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, USA / ACM. Retrieved from http://dl.acm.org/citation.cfm?id=3237383
Market Making via Reinforcement Learning.
Spooner, T., Fearnley, J., Savani, R., & Koukorinis, A. (2018). Market Making via Reinforcement Learning.. In E. André, S. Koenig, M. Dastani, & G. Sukthankar (Eds.), AAMAS (pp. 434-442). International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, USA / ACM. Retrieved from http://dl.acm.org/citation.cfm?id=3237383
2017
GANGs: Generative Adversarial Network Games
Oliehoek, F. A., Savani, R., Gallego-Posada, J., Pol, E. V. D., Jong, E. D. D., & Gross, R. (2017). GANGs: Generative Adversarial Network Games. Retrieved from http://arxiv.org/abs/1712.00679v2
GANGs: Generative Adversarial Network Games
Symmetric Decomposition of Asymmetric Games
Reachability Switching Games
Reachability switching games
Fearnley, J., Gairing, M., Mnich, M., & Savani, R. (2017). Reachability Switching Games. In April (Vol. 22, pp. 2021). Retrieved from http://dx.doi.org/10.23638/LMCS-17(2:10)2021
Computing Constrained Approximate Equilibria in Polymatrix Games
Deligkas., Fearnley., & Savani, R. S. J. (2017). Computing Constrained Approximate Equilibria in Polymatrix Games. In Symposium on Algorithmic Game Theory (SAGT) (pp. 93-105). doi:10.1007/978-3-319-66700-3_8
Lenient Multi-Agent Deep Reinforcement Learning
Lenient Multi-Agent Deep Reinforcement Learning
Palmer, G., Tuyls, K., Bloembergen, D., & Savani, R. (2018). Lenient Multi-Agent Deep Reinforcement Learning. In PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18) (pp. 443-451). Retrieved from https://www.webofscience.com/
LiftUpp: Support to Develop Learner Performance
Oliehoek, F. A., Savani, R., Adderton, E. A., Cui, X., Jackson, D., Jimmieson, P., . . . Dawson, L. (2017). LiftUpp: Support to Develop Learner Performance. In International Journal of Artificial Intelligence in Education. Wuhan, China: International Artificial Intelligence in Education Society. doi:10.1007/978-3-319-61425-0_62
Computing Constrained Approximate Equilibria in Polymatrix Games
LiftUpp: Support to develop learner performance
CLS: New Problems and Completeness
Fearnley, J., Gordon, S., Mehta, R., & Savani, R. (2017). CLS: New Problems and Completeness. Retrieved from http://arxiv.org/abs/1702.06017v2
CLS: New Problems and Completeness
Computing Approximate Nash Equilibria in Polymatrix Games
Deligkas, A., Fearnley, J., Savani, R., & Spirakis, P. (2017). Computing Approximate Nash Equilibria in Polymatrix Games. ALGORITHMICA, 77(2), 487-514. doi:10.1007/s00453-015-0078-7
Computing Constrained Approximate Equilibria in Polymatrix Games.
Deligkas, A., Fearnley, J., & Savani, R. (2017). Computing Constrained Approximate Equilibria in Polymatrix Games.. CoRR, abs/1705.02266.
LiftUpp: Support to Develop Learner Performance
Oliehoek, F. A., Savani, R., Adderton, E., Cui, X., Jackson, D., Jimmieson, P., . . . Dawson, L. (2017). LiftUpp: Support to Develop Learner Performance. In Artificial Intelligence in Education (Vol. 10331, pp. 553-556). Springer Nature. doi:10.1007/978-3-319-61425-0_62
2016
Distributed Methods for Computing Approximate Equilibria
Czumaj, A., Deligkas, A., Fasoulakis, M., Fearnley, J. S., Jurdzinski, M., & Savani, R. (2016). Distributed Methods for Computing Approximate Equilibria. In Lecture Notes in Computer Science Vol. 10123. Berlin: Springer Nature. doi:10.1007/978-3-662-54110-4_2
Inapproximability Results for Approximate Nash Equilibria.
Deligkas, A., Fearnley, J., & Savani, R. (2016). Inapproximability Results for Approximate Nash Equilibria.. In Y. Cai, & A. Vetta (Eds.), WINE Vol. 10123 (pp. 29-43). Springer. Retrieved from https://doi.org/10.1007/978-3-662-54110-4
Inapproximability Results for Approximate Nash Equilibria
Inapproximability results for approximate Nash equilibria
Deligkas, A., Fearnley, J., & Savani, R. (2016). Inapproximability Results for Approximate Nash Equilibria. Retrieved from http://arxiv.org/abs/1608.03574v3
Space Debris Removal: A Game Theoretic Analysis
Klima, R., Bloembergen, D., Savani, R., Tuyls, K., Hennes, D., & Izzo, D. (2016). Space Debris Removal: A Game Theoretic Analysis. Games, 7(3). doi:10.3390/g7030020
Finding Approximate Nash Equilibria of Bimatrix Games via Payoff Queries
Fearnley, J., & Savani, R. (2016). Finding Approximate Nash Equilibria of Bimatrix Games via Payoff Queries. ACM Transactions on Economics and Computation (TEAC), 4(4). doi:10.1145/2956579
Hedonic Games
Aziz, H., & Savani, R. (2016). Hedonic Games. In Handbook of Computational Social Choice (pp. 356-376). Cambridge University Press. doi:10.1017/cbo9781107446984.016
An Empirical Study on Computing Equilibria in Polymatrix Games
An empirical study on computing equilibria in polymatrix games
Deligkas, A., Fearnley, J., Igwe, T. P., & Savani, R. (2016). An Empirical Study on Computing Equilibria in Polymatrix Games. In AAMAS'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (pp. 186-195). Retrieved from https://www.webofscience.com/
Preface
Gairing, M., & Savani, R. (2016). Preface (Vol. 9928 LNCS).
Space Debris Removal: A Game Theoretic Analysis
Klima, R., Bloembergen, D., Savani, R., Tuyls, K., Hennes, D., & Izzo, D. (2016). Space Debris Removal: A Game Theoretic Analysis. In ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE Vol. 285 (pp. 1658-1659). doi:10.3233/978-1-61499-672-9-1658
The Complexity of All-Switches Strategy Improvement
Fearnley, J., & Savani, R. (2016). The Complexity of All-Switches Strategy Improvement. In R. Krauthgamer (Ed.), Proceedings of the 2016 Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 130-139). Society for Industrial and Applied Mathematics. doi:10.1137/1.9781611974331.ch10
2015
Distributed Methods for Computing Approximate Equilibria
Computing Stable Outcomes in Symmetric Additively Separable Hedonic Games
Gairing, M., & Savani, R. (2019). Computing Stable Outcomes in Symmetric Additively Separable Hedonic Games. In MATHEMATICS OF OPERATIONS RESEARCH (Vol. 44, Iss. 3, pp. 1101-1121). doi:10.1287/moor.2018.0960
Computing stable outcomes in symmetric additively-separable hedonic games
Learning equilibria of games via payoff queries
Fearnley, J., Gairing, M., Goldberg, P. W., & Savani, R. (2015). Learning Equilibria of Games via Payoff Queries. JOURNAL OF MACHINE LEARNING RESEARCH, 16, 1305-1344. Retrieved from https://www.webofscience.com/
The Complexity of All-switches Strategy Improvement
An Empirical Study of Finding Approximate Equilibria in Bimatrix Games
Fearnley, J., Igwe, T. P., & Savani, R. (2015). An Empirical Study of Finding Approximate Equilibria in Bimatrix Games. In EXPERIMENTAL ALGORITHMS, SEA 2015 Vol. 9125 (pp. 339-351). doi:10.1007/978-3-319-20086-6_26
An Empirical Study of Finding Approximate Equilibria in Bimatrix Games
Unit Vector Games
Unit vector games
Savani, R., & von Stengel, B. (2016). Unit vector games. INTERNATIONAL JOURNAL OF ECONOMIC THEORY, 12(1), 7-27. doi:10.1111/ijet.12077
An Empirical Study of Finding Approximate Equilibria in Bimatrix Games.
Fearnley, J., Igwe, T. P., & Savani, R. (2015). An Empirical Study of Finding Approximate Equilibria in Bimatrix Games.. In E. Bampis (Ed.), SEA Vol. 9125 (pp. 339-351). Springer. Retrieved from https://doi.org/10.1007/978-3-319-20086-6
2014
Computing Approximate Nash Equilibria in Polymatrix Games
Computing Approximate Nash Equilibria in Polymatrix Games.
Deligkas, A., Fearnley, J., Savani, R., & Spirakis, P. (2014). Computing Approximate Nash Equilibria in Polymatrix Games. In WEB AND INTERNET ECONOMICS Vol. 8877 (pp. 58-71). Retrieved from https://www.webofscience.com/
The Complexity of the Simplex Method
Fearnley, J., & Savani, R. (2015). The Complexity of the Simplex Method. In STOC '15: Proceedings of the forty-seventh annual ACM symposium on Theory of Computing (pp. 201-208). doi:10.1145/2746539.2746558
The Complexity of the Simplex Method
Game Theory Explorer - Software for the Applied Game Theorist
Game Theory Explorer: software for the applied game theorist
Savani, R., & von Stengel, B. (2015). Game Theory Explorer: software for the applied game theorist. COMPUTATIONAL MANAGEMENT SCIENCE, 12(1), 5-33. doi:10.1007/s10287-014-0206-x
A Data Rich Money Market Model - Agent-based Modelling for Financial Stability
Devine, P., & Savani, R. (2014). A Data Rich Money Market Model - Agent-based Modelling for Financial Stability. In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (pp. 231-236). SCITEPRESS - Science and Technology Publications. doi:10.5220/0005096602310236
Computing Approximate Nash Equilibria in Polymatrix Games
Deligkas, A., Fearnley, J., Savani, R., & Spirakis, P. (2014). Computing Approximate Nash Equilibria in Polymatrix Games. In Web and Internet Economics (Vol. 8877, pp. 58-71). Springer Nature. doi:10.1007/978-3-319-13129-0_5
Cooperative Max Games and Agent Failures
Bachrach, Y., Savani, R., & Shah, N. (2014). Cooperative Max Games and Agent Failures. In AAMAS'14: PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (pp. 29-36). Retrieved from https://www.webofscience.com/
Equilibrium Computation (Dagstuhl Seminar 14342)
Megiddo, N., Mehlhorn, K., Savani, R., & Vazirani, V. (2014). Equilibrium Computation (Dagstuhl Seminar 14342). Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. doi:10.4230/DagRep.4.8.73
Finding approximate nash equilibria of bimatrix games via payoff queries
Fearnley, J., & Savani, R. (2014). Finding approximate nash equilibria of bimatrix games via payoff queries. In Proceedings of the fifteenth ACM conference on Economics and computation (pp. 657-674). ACM. doi:10.1145/2600057.2602847
Increasing VCG Revenue by Decreasing the Quality of Items
Guo, M., Deligkas, A., & Savani, R. (2014). Increasing VCG Revenue by Decreasing the Quality of Items. In PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 705-711). Retrieved from https://www.webofscience.com/
2013
Finding Approximate Nash Equilibria of Bimatrix Games via Payoff Queries
Finding approximate Nash equilibria of bimatrix games via payoff queries
Fearnley, J., & Savani, R. (2013). Finding Approximate Nash Equilibria of Bimatrix Games via Payoff Queries. Retrieved from http://arxiv.org/abs/1310.7419v2
Polylogarithmic Supports are required for Approximate Well-Supported Nash Equilibria below 2/3
Anbalagan, Y., Norin, S., Savani, R., & Vetta, A. (2013). Polylogarithmic Supports are required for Approximate Well-Supported Nash Equilibria below 2/3. Retrieved from http://arxiv.org/abs/1309.7258v2
Polylogarithmic Supports are required for Approximate Well-Supported Nash Equilibria below 2/3
Learning equilibria of games via payoff queries
Fearnley, J., Gairing, M., Goldberg, P., & Savani, R. (2013). Learning equilibria of games via payoff queries. In Proceedings of the fourteenth ACM conference on Electronic commerce (pp. 397-414). ACM. doi:10.1145/2482540.2482558
The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions
Goldberg, P. W., Papadimitriou, C. H., & Savani, R. (2013). The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions. ACM Transactions on Economics and Computation, 1(2), 1-25. doi:10.1145/2465769.2465774
Learning Equilibria of Games via Payoff Queries
Learning Equilibria of Games via Payoff Queries
Fearnley, J., Gairing, M., Goldberg, P., & Savani, R. (2013). Learning Equilibria of Games via Payoff Queries. Retrieved from http://arxiv.org/abs/1302.3116v4
Game Theory Explorer
Egesdal, M., Gomez-Jordana, A., Pelissier, C., Savani, R. S. J., von Stengel, B., & Prause, M. (2013). Game Theory Explorer [Computer Software].
Game Theory Explorer
Savani, R. S. J. (2013). Game Theory Explorer [Computer Software]. Retrieved from http://gametheoryexplorer.org/
Polylogarithmic Supports Are Required for Approximate Well-Supported Nash Equilibria below 2/3
Anbalagan, Y., Norin, S., Savani, R., & Vetta, A. (2013). Polylogarithmic Supports Are Required for Approximate Well-Supported Nash Equilibria below 2/3. In Web and Internet Economics (Vol. 8289, pp. 15-23). Springer Nature. doi:10.1007/978-3-642-45046-4_2
2012
Approximate Well-Supported Nash Equilibria Below Two-Thirds
Fearnley, J., Goldberg, P. W., Savani, R., & Sørensen, T. B. (2012). Approximate Well-Supported Nash Equilibria Below Two-Thirds. In Unknown Conference (pp. 108-119). Springer Berlin Heidelberg. doi:10.1007/978-3-642-33996-7_10
High-Frequency Trading: The Faster, the Better?
Savani, R. (2012). High-Frequency Trading: The Faster, the Better?. IEEE INTELLIGENT SYSTEMS, 27(4), 70-73. doi:10.1109/MIS.2012.75
Approximate Well-supported Nash Equilibria Below Two-thirds
Fearnley, J., Goldberg, P. W., Savani, R., & Sørensen, T. B. (2016). Approximate Well-supported Nash Equilibria Below Two-thirds. Algorithmica, 76, 297-319. doi:10.1007/s00453-015-0029-3
Approximate Well-supported Nash Equilibria below Two-thirds
2011
On the Approximation Performance of Fictitious Play in Finite Games
Goldberg, P. W., Savani, R., Sorensen, T. B., & Ventre, C. (2011). On the Approximation Performance of Fictitious Play in Finite Games. In ALGORITHMS - ESA 2011 Vol. 6942 (pp. 93-105). Retrieved from https://www.webofscience.com/
On the Approximation Performance of Fictitious Play in Finite Games
On the approximation performance of fictitious play in finite games
Goldberg, P. W., Savani, R., Sorensen, T. B., & Ventre, C. (2013). On the approximation performance of fictitious play in finite games. INTERNATIONAL JOURNAL OF GAME THEORY, 42(4), 1059-1083. doi:10.1007/s00182-012-0362-6
Computing Stable Outcomes in Hedonic Games with Voting-Based Deviations
Gairing, M., & Savani, R. (2011). Computing Stable Outcomes in Hedonic Games with Voting-Based Deviations. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011) (pp. 559-566). Taipei: -. Retrieved from http://portal.acm.org/
Computing stable outcomes in hedonic games with voting-based deviations
Gairing, M., & Savani, R. (2011). Computing stable outcomes in hedonic games with voting-based deviations. In 10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011 Vol. 1 (pp. 521-528).
2010
Computing Stable Outcomes in Hedonic Games
Gairing, M., & Savani, R. (2010). Computing Stable Outcomes in Hedonic Games. In ALGORITHMIC GAME THEORY Vol. 6386 (pp. 174-185). Retrieved from https://www.webofscience.com/
The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions
The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions.
Goldberg, P. W., Papadimitriou, C. H., & Savani, R. (2011). The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions. In 2011 IEEE 52ND ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS 2011) (pp. 67-76). doi:10.1109/FOCS.2011.26
Linear complementarity algorithms for infinite games
Fearnley, J., Jurdziński, M., & Savani, R. (2010). Linear complementarity algorithms for infinite games. In 36th Conference on Current Trends in Theory and Practice of Computer Science (pp. 382-393). Czech Republic: Springer-Verlag.
2009
Power Indices in Spanning Connectivity Games
Aziz, H., Lachish, O., Paterson, M., & Savani, R. (2009). Power Indices in Spanning Connectivity Games. In ALGORITHMIC ASPECTS IN INFORMATION AND MANAGEMENT, PROCEEDINGS Vol. 5564 (pp. 55-67). Retrieved from https://www.webofscience.com/
Enumeration of Nash equilibria for two-player games
Avis, D., Rosenberg, G. D., Savani, R., & von Stengel, B. (2010). Enumeration of Nash equilibria for two-player games. ECONOMIC THEORY, 42(1), 9-37. doi:10.1007/s00199-009-0449-x
Linear Complementarity Algorithms for Infinite Games
Linear complementarity algorithms for infinite games
Fearnley, J., Jurdzinski, M., & Savani, R. (2010). Linear Complementarity Algorithms for Infinite Games. In SOFSEM 2010: THEORY AND PRACTICE OF COMPUTER SCIENCE, PROCEEDINGS Vol. 5901 (pp. 382-+). Retrieved from https://www.webofscience.com/
Wiretapping a hidden network
Aziz, H., Lachish, O., Paterson, M., & Savani, R. (2009). Wiretapping a Hidden Network. In INTERNET AND NETWORK ECONOMICS, PROCEEDINGS Vol. 5929 (pp. 438-+). Retrieved from https://www.webofscience.com/
Wiretapping a hidden network
Spanning connectivity games
Aziz, H., Lachish, O., Paterson, M., & Savani, R. (2009). Spanning connectivity games. Retrieved from http://arxiv.org/abs/0906.3643v1
Spanning connectivity games
Multi-strategy trading utilizing market regimes
Mlnarik, H., Ramamoorthy, S., & Savani, R. (2009). Multi-strategy trading utilizing market regimes.
Wiretapping a Hidden Network
Aziz, H., Lachish, O., Paterson, M., & Savani, R. (2009). Wiretapping a Hidden Network. In Internet and Network Economics (Vol. 5929, pp. 438-446). Springer Nature. doi:10.1007/978-3-642-10841-9_40
2008
A simple P-matrix linear complementarity problem for discounted games
Jurdzinski, M., & Savani, R. (2008). A simple P-matrix linear complementarity problem for discounted games. In LOGIC AND THEORY OF ALGORITHMS Vol. 5028 (pp. 283-293). doi:10.1007/978-3-540-69407-6_32
Good neighbors are hard to find: computational complexity of network formation
Baron, R., Durieu, J., Haller, H., Savani, R., & Solal, P. (2008). Good neighbors are hard to find: computational complexity of network formation. REVIEW OF ECONOMIC DESIGN, 12(1), 1-19. doi:10.1007/s10058-008-0043-x
2006
Hard-to-solve bimatrix games
Savani, R., & von Stengel, B. (2006). Hard-to-solve bimatrix games. ECONOMETRICA, 74(2), 397-429. doi:10.1111/j.1468-0262.2006.00667.x
'Finding Nash equilibria of bimatrix games'
Savani, R. (2006). 'Finding Nash equilibria of bimatrix games'. (PhD Thesis, London School of Economics and Political Science).
2005
Mixed-species aggregations in birds:: zenaida doves, <i>Zenaida aurita</i>, respond to the alarm calls of carib grackles, <i>Quiscalus lugubris</i>
Griffin, A. S., Savani, R. S., Hausmanis, K., & Lefebvre, L. (2005). Mixed-species aggregations in birds:: zenaida doves, <i>Zenaida aurita</i>, respond to the alarm calls of carib grackles, <i>Quiscalus lugubris</i>. ANIMAL BEHAVIOUR, 70, 507-515. doi:10.1016/j.anbehav.2004.11.023
A novel strategy for the Penn-Lehman automated trading competition
Veal, B., & Savani, R. (2005). A novel strategy for the Penn-Lehman automated trading competition. Retrieved from http://www.cdam.lse.ac.uk/Reports/Abstracts/cdam-2005-12.html
2004
Exponentially many steps for finding a nash equilibrium in a bimatrix game
Savani, R., & von Stengel, B. (2004). Exponentially many steps for finding a nash equilibrium in a bimatrix game. In 45TH ANNUAL IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, PROCEEDINGS (pp. 258-267). doi:10.1109/FOCS.2004.28
Challenge instances for NASH
Savani, R. (2004). Challenge instances for NASH. Retrieved from http://www.cdam.lse.ac.uk/Reports/Abstracts/cdam-2004-14.html
2002
Solve a bimatrix game
Savani, R. (2002). Solve a bimatrix game [Internet (free access)]. Retrieved from http://banach.lse.ac.uk/form.html